Water is an Issue, But Not Because of Data Centers or AI

The near-hysteria about water consumption needs to be kept in proper perspective. In the water-short American West, including the Colorado River watershed, water is always an issue. 

In terms of water access, the United States is effectively divided by the historic 100th meridian, which runs roughly through Texas, Oklahoma, Kansas, Nebraska, and the Dakotas. East of that line, rainfall is generally sufficient to support agriculture without irrigation. West of it, irrigation is necessary.

Region	Typical Annual Precipitation
Pacific Northwest mountains	60–150+ inches
Eastern U.S. (most areas)	30–60 inches
Midwest	25–45 inches
Great Plains	15–35 inches
Intermountain West (Nevada, Utah, Wyoming interior, western Colorado)	5–20 inches
Desert Southwest	3–15 inches

But precipitation alone does not illustrate the issue as well as water runoff, which is the amount of liquid that remains available for use after evaporation and plant transpiration. In much of the Intermountain West and Great Plains, most precipitation evaporates or is consumed by vegetation before reaching streams.

Region	Typical Annual Runoff
Appalachian region	15–40+ inches
Upper Midwest	5–15 inches
Great Plains	0.5–5 inches
Intermountain West basins	Less than 1–3 inches
Desert Southwest	Often less than 0.5 inch

Relative to demand, west of the 100th meridian, water is always going to be an issue. 

Region	Water Supply Relative to Demand
Northeast	Large surplus
Southeast	Large surplus
Great Lakes	Very large surplus
High Plains	Small surplus
Southwest	Deficit
Colorado River Basin	Deficit

So it might be inevitable that water footprint becomes an issue for data centers, even if relative water consumption is quite low. Of course, a total water footprint would include the cost of generating electricity. 

Still, industry uses relatively little water, compared to other sectors of the economy. 

source: Axios 

But an argument can be made that the easiest gains might come from increasing agriculture efficiency where it comes to water consumption. 

And even if controversial, the easiest market encouragement might include shifting our subsidies for agricultural water pricing, as difficult as that will be for many farmers always on the brink of survival. 

As always, rights and values are in tension. Most people might say they believe in supporting family farms, just as much as they might say they value water conservation. But the numbers are clear. Small gains in agriculture will produce more efficiency, faster, than small gains in consumption in other sectors. 

Sector	Share of Water Consumption (Typical Western Basin)
Agriculture	70–80%
Municipal	10–20%
Industry	5–10%

Indeed, water pricing discourages efficiency because the users of 70 percent to 80 percent of the water pay the lowest prices for consumption. Again, values are in conflict. We might value food production and small farms as much as we value drinking water and electricity. 

But there is an order of magnitude difference between agricultural water prices and all urban uses of water. And as with all commodities and goods, low prices encourage consumption; higher prices encourage efficiency. 

Tradable water rights might be a preferred solution, shifting supply towards demand without expropriating or destroying farming. Also, it might make sense to encourage water-intensive agriculture only in regions with lots of water, while discouraging it in regions that are water scarce. 

Again, this will be controversial. 

User Type	Typical Economic Value of Water
Alfalfa irrigation	$50–$300 per acre-foot
Corn irrigation	$100–$500 per acre-foot
Municipal supply	$1,000–$5,000+ per acre-foot
Industrial/high-value uses	Often much higher

In other words, does it make good sense to grow water-intensive rice, almonds or alfalfa in water-scarce regions?

Crop	Acre-Feet per Acre
Wheat	1–2
Corn	2–3
Alfalfa	3–6
Almonds	3–4
Rice	4–5

As if that were not complicated enough, we also must balance protection of wetlands, fisheries, recreation and food sourcing. 

Data center water consumption might be an issue, but a relatively small one, overall. How we use and price use of a scarce resource is really the bigger issue. 

Does Generative AI Use Stunt Cognitive Skill Development in Children?

We might still not know whether using generative artificial language models has any negative effect on cognitive skills, but Norway believes elementary school children should not be using it, and has banned it. 

Some studies suggest possible danger, but still inconclusive.

Study	Population	Key Finding	Relevance to Elementary Students
Bastani et al. (2025)	Nearly 1,000 high-school math students	Students using a standard GPT-4 tutor performed better while AI was available, but later performed 17% worse when AI access was removed. Researchers concluded that AI can become a "crutch" that impairs learning if poorly designed. (Scale)	Indirect evidence; suggests similar risks for younger learners who may rely heavily on AI assistance.
MIT Media Lab study (2025)	Adults (18–39) writing essays	AI users showed lower brain engagement, weaker memory of their own work, and reduced independent performance compared with non-AI users. Findings remain preliminary and are not specific to children. (MIT News)	Raises concerns that habitual AI use may reduce cognitive effort during learning tasks.
UNESCO (2023/2026 update)	Policy review	Warns that overreliance on generative AI may compromise the development of intellectual and social skills. Recommends age-appropriate use and safeguards. (UNESCO)	Directly discusses risks for children and recommends restrictions and human oversight.
Jaemarie Solyst et al. (2024)	26 middle-school girls	Participants initially showed substantial overtrust in ChatGPT outputs. Exposure to AI errors reduced that trust. (arXiv)	Suggests children may have difficulty evaluating AI-generated information critically.
Systematic review of generative AI in elementary education (2025)	Review of studies from 2020–2025	Found potential benefits but noted limited evidence, concerns about dependence, misinformation, and the need for teacher supervision. (Shanti Bhuana Journal)	One of the few reviews focused specifically on elementary education.

The emerging evidence points to three main concerns:

• Reduced productive struggle

• Learning often requires effortful practice, problem solving, and making mistakes.

• If AI supplies answers too quickly, students may skip the cognitive processes that build durable understanding. The high-school math study provides the strongest experimental evidence for this concern. (Scale)

• Cognitive offloading

• Researchers describe a phenomenon where people rely on external tools instead of developing internal knowledge and reasoning skills.

• Recent MIT findings suggest heavy AI assistance may reduce engagement and memory formation during learning activities. (MIT News)

• Overtrust and misinformation

• Children may be particularly vulnerable to accepting AI-generated content as authoritative.

• Studies of young users show that they can initially place excessive trust in chatbot outputs. (arXiv)

• 
  

But the evidence is not one-sided:

• Some studies find that AI tutors can substantially improve learning when designed to guide students through reasoning rather than simply provide answers. (Scale)

• The strongest "harm" findings generally occur when AI acts as an answer machine rather than a scaffold for thinking. (Scale)

• There is currently little direct experimental evidence involving elementary school children, so claims that generative AI definitely impairs learning in that age group remain tentative. Most experts argue that the impact depends heavily on how AI is designed and supervised. (UNESCO).

Current research does not show that generative AI inevitably harms elementary-school learning. 

However, several studies and policy reviews suggest that unsupervised or answer-focused AI use may impair skill development, critical thinking, and knowledge retention, particularly when students rely on it instead of engaging in the learning process themselves.

The strongest evidence so far comes from older students, while evidence specific to elementary-aged children remains limited and is still developing. (Scale). 

Are Happy Workers More Productive? Maybe, Sometimes.

Most of us instinctively assume that “happy” workers must be more productive, and while that can be true, it might often be the case that even happy workers are not necessarily more productive. 

Happiness seems to matter more for some jobs than others, especially knowledge work, creative work, sales, and customer-facing roles.

But there are lots of other issues, ranging from poor management to misaligned goals, skills or incentives.

Study	Sample / Method	Key Finding
Andrew J. Oswald, Eugenio Proto, and Daniel Sgroi (2015)	Controlled experiments	Workers randomly induced into a happier mood were about 12 percent more productive than controls. Evidence supports a causal effect from happiness to productivity. (Chicago Journals)
"Happy Productive Worker" research synthesis (2025)	Review of 33 studies across 27 countries	Overall evidence supports a positive relationship between worker happiness and productivity, though effect sizes vary by occupation and measurement method. (Springer)
Gallup Q12 Meta-analysis	736 studies, 100,000+ teams, 2.7 million employees	Employee engagement is strongly associated with higher productivity, profitability, retention, customer satisfaction, and lower absenteeism. (Gallup.com)
Software Developer studies (Graziotin et al.)	Programming tasks and developer surveys	Positive emotional states correlate with higher self-assessed productivity and better cognitive performance. (arXiv)
Positive Feedback study (2023)	Professional workers in real environments	Positive feedback improved subsequent performance; negative feedback generally did not. (arXiv)

Many workplace studies suffer from a classic problem: are people productive because they are happy, or happy because they are productive? In other words, is there a causal relationship, and, if so, in what direction?

Several mechanisms appear repeatedly in the literature, but they do not all have to do with “happiness.”

Mechanism	Effect on Productivity
Better concentration	Fewer errors
More energy	Higher output
Greater persistence	Less quitting when tasks become difficult
Better collaboration	More effective teamwork
More creativity	Better problem-solving
Lower stress	Improved cognitive performance
Lower absenteeism	More hours worked

These effects tend to matter most where human judgment is important, in a few situations:

• Knowledge Work (Engineers, consultants, researchers, designers, analysts, and software developers appear particularly sensitive to emotional state because productivity depends heavily on cognition and creativity. (arXiv

• Sales and customer service (Positive moods can improve interactions with customers, influencing sales and retention, as the experience is, in many ways, the product)

• Team-Based Work (engagement and morale can affect coordination and cooperation (Gallup.com). 

So even if good advice is to attempt to creation environments where workers are happy, there are lots of other input variables, where the goal is higher productivity.

But “productivity” is not directly produced by:

• friendly culture

• generous benefits

• satisfied employees. 

Many startups, investment banks, law firms, and military organizations have historically produced high output despite significant stress and only moderate happiness.

Likewise, some comfortable organizations generate little value.

The evidence suggests that engagement is often a better predictor than simple happiness.

If “happiness” is "I feel good," then engagement is "I care about this work."

An employee can be:

• happy but disengaged

• engaged but stressed

• both engaged and happy. 

• 
  

Research generally finds that engagement is more closely tied to organizational performance than simple job satisfaction, according to Gallup.com.

So the causal chain is not so much “happy workers are productive workers,” but something more like “competent workers, meaningful work, supportive management and positive well-being lead to higher productivity. 

An interesting economic observation is that happiness often functions less like a direct production input and more like a multiplier on human capital.

For example:

Worker Type	Skill Level	Happiness Effect
Routine factory task	Moderate	Small-to-moderate
Call center worker	Moderate	Moderate
Salesperson	High	Significant
Software engineer	High	Significant
Research scientist	Very high	Very significant

Happy workers are more likely to be productive, especially in knowledge-intensive jobs, but productivity depends on a broader combination of skills, incentives, engagement, management quality, and organizational design. Happiness helps, but it is not sufficient by itself.